The beauty of machine learning? It never stops learning

Business not using machine learning to augment the products and services will find it difficult to compete in the future according to panelists at GigaOm’s Structure:Data event on Wednesday. Not only will these companies be at a competitive disadvantage at first, it will get worse. Why? Machine learning solutions will only gain more intelligence with additional data and techniques.

Advertisement

Currie Boyle, a Distinguished Engineer at IBM(s IBM), explained how natural language processing and continual machine learning transforms online sellers, allowing them to interact with its customers. “These products help with guided selling on the web across online retail sites. More importantly, they try to understand the unsuccessful transactions to improve machine learning. That can transform clients from being relatively static to human-like; the more it’s used, the more successful for you and others.”

The beauty is that such machine learning solutions can help both buyers and sellers in a transaction in ways that weren’t possible just a decade ago. Mok Oh, Chief Scientist at PayPal (s ebay) noted this: “Human loops trying to match buyers and sellers is inefficient. Machine learning makes it scalable and cost-effective to connect them in a world of unstructured data across millions of sites and products.”

While this machine learning sounds like a magical solution, it’s not bulletproof because it depends on human observation that varies from inputs. Amarnath Thombre, SVP, Strategy and Analytics at Match.com believes strongly that successful businesses will be those that observe their customers. But the company found a problem: Match.com customer profiles sometimes differed from how those customers behaved in real life.

“There’s no one single formula to match people, but we had millions of data points for successful matches,” Thombre said. “Mining it provided us an equation for success but that wasn’t enough. We saw a 2x increase in success after modifying our machine learning algorithms to account for the differences that customers had in their profile vs real life.”

Although machine learning is powering success, not all business are using it. That’s a problem says Alexander Gray, CTO, Skytree, suggesting that in the future every business will be a data-driven enterprise. “Every organization will need to do machine learning to augment human decisions” As data grows and machine learning improves, however, that will require more computing horsepower and multiple iterations of data interpretation.

Curry summed it up best by saying that this constant learning can help retain customers, even as barriers for customer departures are decreasing. “Models change every quarter so machine learning has to continue learning. We need to keep getting the right data sets and asking the right questions for machine learning to help customer retention.”

Overall, I agree. Machine learning should improve over time, and it’s hard to argue (for reasons other than cost) against having the capability. However, there will be situations in which a machine will not be able to improve beyond a certain point – local optimizations and ambiguities (even false data as Thombre aptly points out) will limit the final accuracy of a pure machine solution. Ultimately subjective judgment does matter for something.